A new method to mitigate multipath error in single-frequency GPS receiver with wavelet transform

GPS Solutions ◽  
2013 ◽  
Vol 18 (2) ◽  
pp. 189-198 ◽  
Author(s):  
M. R. Azarbad ◽  
M. R. Mosavi
Keyword(s):  
Wavelet Transform ◽  
New Method ◽  
Single Frequency ◽  
Gps Receiver ◽  
Multipath Error

scholarly journals Performance evaluation of low-cost GPS systems for static and kinematic applications

2021 ◽  
Author(s):  
Amit Joshi
Keyword(s):  
Wavelet Analysis ◽  
Low Cost ◽  
Original Data ◽  
Single Frequency ◽  
Data Sets ◽  
Gps Receiver ◽  
Positional Accuracy ◽  
Kinematic Data ◽  
Multipath Error ◽  
Static Data

This thesis looks at improving positional accuracy of low-cost systems by investigating a method to isolate the multipath error based on wavelet analysis. Several sets of static and kinematic data were collected in different types of environment using a single-frequency GPS receiver. The code minus carrier combination of the GPS observables was exploited. After accounting for certain errors and resolving the ionospheric delay using ionospheric maps, the remaining terms were essentially multipath and noise. Wavelet analysis was then used to extract the multipath error. These approximations were utilized to identify and remove those satellites that were severely contaminated with multipath. Another approach investigated the subtraction of multipath approximations obtained by wavelet analysis from the corresponding code measurements. The positioning results of these two approaches were compared with those of the original data and assessed. For the static data sets, eliminating satellites contaminated with multipath proved to be most effective. For the kinematic sessions, neither of the two approaches displayed any improvement.

scholarly journals Performance evaluation of low-cost GPS systems for static and kinematic applications

2021 ◽  
Author(s):  
Amit Joshi
Keyword(s):  
Wavelet Analysis ◽  
Low Cost ◽  
Original Data ◽  
Single Frequency ◽  
Data Sets ◽  
Gps Receiver ◽  
Positional Accuracy ◽  
Kinematic Data ◽  
Multipath Error ◽  
Static Data

This thesis looks at improving positional accuracy of low-cost systems by investigating a method to isolate the multipath error based on wavelet analysis. Several sets of static and kinematic data were collected in different types of environment using a single-frequency GPS receiver. The code minus carrier combination of the GPS observables was exploited. After accounting for certain errors and resolving the ionospheric delay using ionospheric maps, the remaining terms were essentially multipath and noise. Wavelet analysis was then used to extract the multipath error. These approximations were utilized to identify and remove those satellites that were severely contaminated with multipath. Another approach investigated the subtraction of multipath approximations obtained by wavelet analysis from the corresponding code measurements. The positioning results of these two approaches were compared with those of the original data and assessed. For the static data sets, eliminating satellites contaminated with multipath proved to be most effective. For the kinematic sessions, neither of the two approaches displayed any improvement.

scholarly journals High-precision coseismic displacement estimation with a single-frequency GPS receiver

2015 ◽  
Vol 202 (1) ◽  
pp. 612-623 ◽  
Author(s):  
Bofeng Guo ◽  
Xiaohong Zhang ◽  
Xiaodong Ren ◽  
Xingxing Li
Keyword(s):  
High Precision ◽  
Single Frequency ◽  
Gps Receiver ◽  
Coseismic Displacement ◽  
Displacement Estimation

Detection of Spoofing Attack using Machine Learning based on Multi-Layer Neural Network in Single-Frequency GPS Receivers

2017 ◽  
Vol 71 (1) ◽  
pp. 169-188 ◽  
Author(s):  
E. Shafiee ◽  
M. R. Mosavi ◽  
M. Moazedi
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Late Phase ◽  
Machine Learning Algorithms ◽  
Computational Method ◽  
Single Frequency ◽  
Detection Accuracy ◽  
Gps Receiver ◽  
Navigation Data ◽  
Spoofing Detection

The importance of the Global Positioning System (GPS) and related electronic systems continues to increase in a range of environmental, engineering and navigation applications. However, civilian GPS signals are vulnerable to Radio Frequency (RF) interference. Spoofing is an intentional intervention that aims to force a GPS receiver to acquire and track invalid navigation data. Analysis of spoofing and authentic signal patterns represents the differences as phase, energy and imaginary components of the signal. In this paper, early-late phase, delta, and signal level as the three main features are extracted from the correlation output of the tracking loop. Using these features, spoofing detection can be performed by exploiting conventional machine learning algorithms such as K-Nearest Neighbourhood (KNN) and naive Bayesian classifier. A Neural Network (NN) as a learning machine is a modern computational method for collecting the required knowledge and predicting the output values in complicated systems. This paper presents a new approach for GPS spoofing detection based on multi-layer NN whose inputs are indices of features. Simulation results on a software GPS receiver showed adequate detection accuracy was obtained from NN with a short detection time.

scholarly journals A New Method for Fundamental Signal Extraction Based on Wavelet Transform

Automatika ◽  
2016 ◽  
Vol 57 (2) ◽  
Author(s):  
Javad Modarresi ◽  
Eskandar Gholipour
Keyword(s):  
Wavelet Transform ◽  
New Method ◽  
Signal Extraction
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